Image denoising is a challenging problem that is not readily solved. A digital image can include noise in the form of electronic noise, such as may be introduced into the image by the electronics and/or sensor of a digital camera that is used to capture the image. Another common cause of image noise is when an image is captured in low light. Much like a grainy photograph taken with a conventional camera in a low light environment, noise can appear as random specks in a digital image that has been captured with a digital camera in a low light environment. Noise may also be introduced into a digital image during image processing, such as when a compression technique is applied. Noise in a digital image reduces image detail and clarity, and is likely most notable when the image is displayed on a larger size monitor, rather than on a smaller display device that may be integrated with a digital camera, mobile phone, or other portable media device.
Conventional approaches to eliminating or reducing the noise in an image, referred to as denoising the image, leverages image priors, such as utilizing patch recurrence in an image. Patch recurrence is based on similar patches that recur in the same image (e.g., internal patches) and/or similar patches that recur in different, other images (e.g., external patches). The current techniques attempt to leverage one or the other of the two types of priors, the internal patches or the external patches. A technique that leverages the internal, similar patches in an image can still introduce artifacts or blurring effects in the image when there is not a sufficient number of self-similar patches in the image. A technique that leverages the external, similar patches does not perform well with images having only a small or limited noise because other internal patches in the image are difficult to sample and model.